Fast Extraction of Article Titles from XML Based Large Bibliographic Datasets

Swaraj, K. P.; Manjula, D.

doi:10.1016/j.protcy.2016.05.108

Cited by 1 publication

(2 citation statements)

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“…Despite the metamodel allows the representation of instances from any data source, extraction methods for each of these sources still have to be defined. Studies that focus on specific data extraction [61][62][63] can still provide some clues to expand the scope of explored data. Nevertheless, one of the main advantages of the presented system resides in its interoperability.…”

Section: Framework Specification For Text Processingmentioning

confidence: 99%

“…• Word vectorisation and matching methods based on semantic distance computation and Wordnet [63] reference matching to evaluate which extracted entities are semantically close to each others. The end goal of the matching step is to identify new instances by comparing them to instances that have been directly extracted from hyponymy relation extraction.…”

Section: Framework Specification For Text Processingmentioning

confidence: 99%

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A generic metamodel for data extraction and generic ontology population

Chasseray

Barthe‐Delanoë

Negny

et al. 2021

Journal of Information Science

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As the next step in the development of intelligent computing systems is the addition of human expertise and knowledge, it is a priority to build strong computable and well-documented knowledge bases. Ontologies partially respond to this challenge by providing formalisms for knowledge representation. However, one major remaining task is the population of these ontologies with concrete application. Based on Model-Driven Engineering principles, a generic metamodel for the extraction of heterogeneous data is presented in this article. The metamodel has been designed with two objectives, namely (1) the need of genericity regarding the source of collected pieces of knowledge and (2) the intent to stick to a structure close to an ontological structure. As well, an example of instantiation of the metamodel for textual data in chemistry domain and an insight of how this metamodel could be integrated in a larger automated domain independent ontology population framework are given.

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Section: Framework Specification For Text Processingmentioning

confidence: 99%

Section: Framework Specification For Text Processingmentioning

confidence: 99%